Research Engineer: Provenance & Workflow Systems

Aimlroles; Cambridge (MA); Sur place; USD 148 000 - 240 000; Plein temps

Avantages offerts par ce poste

Medical Insurance

Dental Insurance

Vision Insurance

Life Insurance

Disability Insurance

Flexible PTO & Holidays

Parental Leave

Educational Assistance

Commuter Benefits

Bike Share

Lunch Program

Résumé du poste

Lila Sciences is building Scientific Superintelligence and an operating system for science that accelerates discovery across medicine, materials, and energy. This role focuses on provenance to trace inputs, code, models, and results, enabling reproducibility and reuse across complex simulations and ML workloads.

You will design infrastructure, data models, and libraries that empower scientists and engineers to share and analyze results, while aligning with CI/CD, testing, and scalable deployment

Qualifications

• Strong Python engineering skills with a track record of designing and shipping maintainable systems.
• Experience with data modeling - schemas, metadata, identifiers, versioning for data that changes over time.
• Experience designing APIs and building libraries that other engineers or scientists depend on.
• Experience supporting research workflows (ML experiments, simulations, data pipelines) with a clear sense of what makes results reproducible.

Responsabilités

• Build shared provenance infrastructure connecting inputs, code versions, and results across workflows.
• Create data models and metadata schemas for runs, artifacts, and results.
• Develop reusable Python libraries and APIs for storage and metadata systems.
• Turn agent traces into durable, queryable records.
• Translate research prototypes into production-grade tooling and documentation.

Connaissances

Python engineering

Data modeling

API design

Research workflows

Outils

Kubernetes

Containers

CI/CD

Flyte

Ray

Argo

Airflow

Temporal

Dagster

MLflow

Weights & Biases

OpenLineage

DVC

Description du poste

Lila Sciences is building Scientific Superintelligence and an operating system for science that accelerates discovery across medicine, materials, and energy. This role focuses on provenance to trace inputs, code, models, and results, enabling reproducibility and reuse across complex simulations and ML workloads.

You will design infrastructure, data models, and libraries that empower scientists and engineers to share and analyze results, while aligning with CI/CD, testing, and scalable deployment